Desheng Zhang

Exploring human mobility with multi-source data at extremely large metropolitan scales

Expanding our knowledge about human mobility is essential for building efficient wireless protocols and mobile applications. Previous human mobility studies have typically been built upon empirical single-source data (e.g., cellphone or transit data), which inevitably introduces a bias against residents not contributing this type of data, e.g., call detail records cannot be obtained from the residents without cellphone activities, and transit data cannot cover the residents who walk or ride private vehicles. To address this issue, we propose and implement a novel architecture mPat to explore human mobility using multi-source data. A reference implementation of mPat was developed at an unprecedented scale upon the urban infrastructures of Shenzhen, China. The novelty and uniqueness of mPat lie in its three layers: (i) a data feed layer consisting of real-time data feeds from 24 thousand vehicles, 16 million smart cards and 10 million cellphones; (ii) a mobility abstraction layer exploring the correlation and divergence among the multi-source data to analyze and infer human mobility; and (iii) an application layer to improve urban efficiency based on the human mobility findings of the study. The evaluation shows that mPat achieves a 75% inference accuracy, and that its real-world application reduces passenger travel time by 36%.

coRide: carpool service with a win-win fare model for large-scale taxicab networks

Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver co-riders. Although ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this paper, we present the first systematic work to design, implement, and evaluate a carpool service, called coRide, in a large-scale taxicab network intended to reduce total mileage for less gas consumption. Our coRide system consists of three components, a dispatching cloud server, passenger clients, and an onboard customized device, called TaxiBox. In the coRide design, in response to the delivery requests of passengers, dispatching cloud servers calculate cost-efficient carpool routes for taxicab drivers and thus lower fares for the individual passengers. To improve coRides efficiency in mileage reduction, we formulate a NP-hard route calculation problem under different practical constraints. We then provide (i) an optimal algorithm using Linear Programming, (ii) a 2 approximation algorithm with a polynomial complexity, and (iii) its corresponding online version. To encourage coRides adoption, we present a win-win fare model as the incentive mechanism for passengers and drivers to participate. We evaluate coRide with a real world dataset of more than 14,000 taxicabs, and the results show that compared with the ground truth, our service can reduce 33% of total mileage; with our win-win fare model, we can lower passenger fares by 49% and simultaneously increase driver profit by 76%.

EQS: Neighbor Discovery and Rendezvous Maintenance with Extended Quorum System for Mobile Sensing Applications

In many mobile sensing applications devices need to discover new neighbors and maintain the rendezvous with known neighbors continuously. Due to the limited energy supply, these devices have to cycle their radios to conserve energy, making neighbor discovery and rendezvous maintenance even more challenging. To date, the main mechanism for device discover and rendezvous maintenance in existing solutions is pair wise, direct one-hop communication. We argue that such pair wise direct communication is sufficient but not necessary: there exist unnecessary active slots that can be eliminated, without affecting discovery and rendezvous. In this work, we propose a novel concept of extended quorum system, which leverages indirect discovery to further conserve energy. Specifically, we use quorum graph to capture all possible information flow paths where knowledge about known-neighbors can propagate among devices. By eliminating redundant paths, we can reduce the number of active slots significantly. Since a quorum graph can characterize arbitrary active schedules of mobile devices, our work can be broadly used to improve many existing quorum based discovery and rendezvous solutions. The simulation and test bed experimental results show that our solution can reduce as much as 55% energy consumption with a maximal 5% increase in latency for existing solutions.

UrbanCPS: a cyber-physical system based on multi-source big infrastructure data for heterogeneous model integration

Data-driven modeling usually suffers from data sparsity, especially for large-scale modeling for urban phenomena based on single-source urban infrastructure data under fine-grained spatial-temporal contexts. To address this challenge, we motivate, design and implement UrbanCPS, a cyber-physical system with heterogeneous model integration, based on extremely-large multi-source infrastructures in a Chinese city Shenzhen, involving 42 thousand vehicles, 10 million residents, and 16 million smartcards. Based on temporal, spatial and contextual contexts, we formulate an optimization problem about how to optimally integrate models based on highly-diverse datasets, under three practical issues, i.e., heterogeneity of models, input data sparsity or unknown ground truth. We further propose a real-world application called Speedometer, inferring real-time traffic speeds in urban areas. The evaluation results show that compared to a state-of-the-art system, Speedometer increases the inference accuracy by 21% on average.

Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.

pCruise: Reducing Cruising Miles for Taxicab Networks

In taxicab industry, a long standing challenge is how to reduce taxicabs mileage spent without a fare, i.e., cruising mile. The current solution for this challenge usually requires the participation of the passengers. To solve the issue without the passengers involved, in this paper, we propose a cruising system, pCruise, for taxicab drivers to maximize theirs profits by finding the optimal route to pick up a passenger, thus reducing the cruising mile. In pCruise, base on collected GPS records about other near taxicabs, a taxicab characterizes its cruising process with a cruising graph. When a taxicab becomes vacant and tries to find a passenger, cruising graph will provide the shortest cruising route with at least one expected available passengers for this taxicab. With the shortest cruising routes, taxicabs will significantly reduce theirs cruising miles. We evaluate pCruise based on a 7 days 10 GB real world GPS dataset from a city with more than 15,000 taxicabs. The evaluation results show that pCruise can assist taxicab drivers to reduce cruising miles by 41% on average.

Dmodel: Online Taxicab Demand Model from Big Sensor Data in a Roving Sensor Network

Investigating passenger demand is essential for the taxicab business. Existing solutions are typically based on dated and inaccurate offline data collected by manual investigations. To address this issue, we propose Dmodel, using roving taxicabs as real-time mobile sensors to (i) infer passenger arriving moments by interactions of vacant taxicabs, and (ii) infer passenger demand by a customized online training with both historical and real-time data. Such huge taxicab data (almost 1TB per year) pose a big data challenge. To address this challenge, model employs a novel parameter called pickup pattern (accounts for various real-world logical information, e.g., bad weather) to increase the inference accuracy. We evaluate Dmodel with a real-world 450 GB dataset of 14, 000 taxicabs, and results show that compared to the ground truth, Dmodel achieves a 76% accuracy on the demand inference and outperforms a statistical model by 39%.

CallCab: A unified recommendation system for carpooling and regular taxicab services

Carpooling taxicab services hold the promise of providing additional transportation supply, especially in extreme weather or rush hour when regular taxicab services are insufficient. Although many recommendation systems about regular taxicab services have been proposed recently, little research, if any, has been done to assist passengers to find a successful taxicab ride with carpooling. In this paper, we present the first systematic work to design a unified recommendation system for both regular and carpooling services, called CallCab, based on a data driven approach. In response to a passengers request, CallCab aims to recommend either (i) a vacant taxicab for a regular service with no detour, or (ii) an occupied taxicab heading to the similar direction for a carpooling service with less detour, yet without assuming any knowledge of destinations of passengers already on occupied taxicabs. To analyze these unknown destinations of occupied taxicabs, CallCab generates and refines taxicab trip distributions based on GPS datasets and context information collected in the existing taxicab infrastructure. To improve CallCabs efficiency to process such a big dataset, we augment the efficient MapReduce model with a Measure phase tailored for our application. We evaluate CallCab with a real world dataset of 14,000 taxicabs, and results show that compared to ground truth, CallCab can reduce 64% of the total mileage to deliver all passengers and 63% of passengers waiting time.

Taxi dispatch with real-time sensing data in metropolitan areas: a receding horizon control approach

Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.

coMobile: real-time human mobility modeling at urban scale using multi-view learning

Real-time human mobility modeling is essential to various urban applications. To model such human mobility, numerous data-driven techniques have been proposed. However, existing techniques are mostly driven by data from a single view, e.g., a transportation view or a cellphone view, which leads to over-fitting of these single-view models. To address this issue, we propose a human mobility modeling technique based on a generic multi-view learning framework called coMobile. In coMobile, we first improve the performance of single-view models based on tensor decomposition with correlated contexts, and then we integrate these improved single-view models together for multi-view learning to iteratively obtain mutually-reinforced knowledge for real-time human mobility at urban scale. We implement coMobile based on an extremely large dataset in the Chinese city Shenzhen, including data about taxi, bus and subway passengers along with cellphone users, capturing more than 27 thousand vehicles and 10 million urban residents. The evaluation results show that our approach outperforms a single-view model by 51% on average.